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Macroeconometrics

Nowcasting and Forecasting Extreme Events

Real-Time Insights: Nowcasting and Forecasting in Uncertain Times.

20h (10 days)

Online

English

Next edition: March 8-19, 2027

Early bird deadline: January 20, 2027

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Macroeconometrics

Nowcasting and Forecasting Extreme Events

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Who is this Course for?

Nowcasting and Forecasting with High-Frequency Real-Time Data

Course overview

Recent years have shown how fast a shock can move —financial crises, pandemics, energy disruptions, geopolitical shifts can reshape an economy in days, long before traditional quarterly data catch up. This course teaches you to keep pace, using high-frequency and alternative data to detect, measure and forecast economic activity in real time.

Over two weeks, you will build the modern toolkit for real-time macroeconomic analysis from the ground up: state-space models and the Kalman filter, mixing data of different frequencies, dynamic factor models and principal components, weekly and high-frequency activity trackers, nonlinear and regime-switching models, text and sentiment indicators, and machine-learning methods for forecasting. The course reaches the current frontier of activity measurement —tracking the economy at hourly, neighborhood-level resolution— and extends toward early-warning methods that help anticipate disruptions, not just measure them once they arrive.

The approach is hands-on throughout. Every technique is taught alongside the code that implements it, on real data, so that you are able to reproduce and adapt the methods to your own problems. You are encouraged to bring a question of your own and work on it during the course.

Faculty

Discover what makes this Nowcasting and Forecasting course exceptional

1

Real-Time Economic Monitoring: Learn to work with high-frequency data for early detection of shocks and rapid situational analysis.

2

The Granularity Frontier: Reach the current state of the art in activity measurement, tracking the economy at hourly, neighborhood-level resolution

3

Code, Not Just Concepts: Go beyond linear models with nonlinear, quantile and tree-based methods built for prediction and extreme outcomes.

4

Bring Your Own Problem:Apply the techniques directly to a question of your own and work on it during the course.

5

Taught by active researchers:Learn from instructors working at the research frontier of real-time macroeconometrics

Who is this course for?

Practitioners at central banks as well as other private and public institutions which need to provide economic analysis in real time
Masters and PhD students in economics or related fields that want to incorporate in their work real time tools or want to develop new tools to analyze economic activity
Researchers that want to learn about uncertainty and decision making in real time
Students in any quantitative field that want an overview of the techniques used in real economic analysis and incorporate these techniques to their own field

Learning outcomes

The course provides a state-of-the-art, fully reproducible toolkit for real-time economic analysis, from high-frequency nowcasting to machine-learning forecasting. Participants develop the skills to interpret emerging economic developments, anticipate what comes next, and support timely decision-making in a volatile economic environment.

By the end of this course, participants will be able to:

Build real-time nowcasts and forecasts of key macroeconomic variables using high-frequency and alternative data
Apply the core toolkit of real-time analysis: state-space models, the Kalman filter, mixed-frequency data, and dynamic factor models
Work with high-frequency activity trackers, up to the current frontier of hourly, neighborhood-level measurement
Use nonlinear and regime-switching models, and text-based sentiment indicators, to capture features that linear models miss
Apply machine-learning methods —including quantile and tree-based approaches— to macroeconomic forecasting and the prediction of extreme outcomes
Identify turning points in real time and read the early signals that precede major disruptions

Key topics for Nowcasting and Forecasting with High-Frequency Real-Time Data course

Take a look at the themes covered during this course.

The Real-time Toolkit

State-space models and the Kalman filter
Mixing data of different frequencies
VARs
Principal components and dynamic factor models

Tracking Activity at High Frequency

Weekly economic activity trackers and their construction
Introducing high-frequency information into forecasting
The granularity frontier: hourly and neighbourhood-level activity measurement with spatio-temporal factor models
Real-time scenario analysis

Turning Points and the Build-Up of Imbalances

Identifying turning points in real time
Moving from flows to stocks to identify the imbalances that precede major disruptions

Working With Messy, High-Frequency Data

Seasonal adjustment in high-frequency environments
Data selection and signal-to-noise issues

Nonlinear Methods

Markov-switching and regime-change models
Time-varying parameters
MIDAS and threshold models
Quantile regression

Text and Alternative Data

Text and sentiment analysis
Sentiment indicators for real-time monitoring
Web scraping and Google Trends data

Machine Learning for Macroeconomic Forecasting

Regularization and tree-based methods
Quantile regression forests for predicting extreme outcomes
Machine learning in practice and nowcasting benchmarks

Course Materials and Software

MATLAB License

Every participant will receive a time-limited personal free MATLAB license before the course starts. You’ll need to install it on your own computer for practical sessions
Additional materials will be provided, and instructors will be available to discuss your research ideas and projects throughout the course

List of References

Below are some resources that may help you prepare for the course.

References

Take a look at the list of references which may help you prepare for this course.

The references are organized by topic.

General References

Hamilton, J. (1994). Time Series Analysis. Princeton University Press.
Kim, C.-J. and Nelson, C. R. (1999). State-Space Models with Regime Switching. MIT Press.
Lütkepohl, H. (2005). New Introduction to Multivariate Time Series Analysis. Springer.
Canova, F. (2007). Methods for Applied Macroeconomic Research. Princeton University Press.

Real-Time Toolkit and High-Frequency Tracking

Camacho, M. and Pérez-Quirós, G. (2010). “Introducing the euro-STING: Short-term indicator of euro area growth.” Journal of Applied Econometrics, 25(4).
Bai, J., Ghysels, E. and Wright, J. (2013). “State space models and MIDAS regressions.” Econometric Reviews, 32(7).
Schorfheide, F. and Song, D. (2021). “Real-time forecasting with a (standard) mixed-frequency VAR during a pandemic.” NBER Working Paper 29535.
Lewis, D., Mertens, K. and Stock, J. (2022). “Monitoring real activity in real time: The Weekly Economic Index.” Federal Reserve Bank of New York.
Ghezzi, F., Timmermann, A. and Yang, M. (2026). “Gauging hourly economic activity in your neighborhood.” CEPR Discussion Paper DP21098.

Turning Points and the Build-Up of Imbalances

Gadea Rivas, M. D. and Pérez-Quirós, G. (2015). “The failure to predict the great recession—a view through the role of credit.” Journal of the European Economic Association, 13(3), 534–559.
Gadea Rivas, M. D., Laeven, L. and Pérez-Quirós, G. (2020). “Growth-and-risk trade-off.” ECB Working Paper No. 2397.

Nonlinear Methods

Eo, Y. and Kim, C.-J. (2016). “Markov-switching models with evolving regime-specific parameters: Are postwar booms or recessions all alike?” Review of Economics and Statistics, 98(5), 940–949.
Baumeister, C., Leiva-León, D. and Sims, E. (2021). “Tracking weekly state-level economic conditions.” NBER Working Paper 29003.
Leiva-León, D., Pérez-Quirós, G. and Rots, E. (2024). “Real-time weakness of the global economy.” Journal of Applied Econometrics, 39(5), 813–832.

Text and Alternative Data

Shapiro, A. H., Sudhof, M. and Wilson, D. J. (2022). “Measuring news sentiment.” Journal of Econometrics, 228(2), 221–243.
Ashwin, J., Kalamara, E. and Saiz, L. (2024). “Nowcasting euro area GDP with news sentiment: A tale of two crises.” Journal of Applied Econometrics, 39(5), 887–905.

Machine Learning For Forecasting

Adrian, T., Boyarchenko, N. and Giannone, D. (2019). “Vulnerable growth.” American Economic Review, 109(4), 1263–1289.
Babii, A., Ghysels, E. and Striaukas, J. (2022). “Machine learning time series regressions with an application to nowcasting.” Journal of Business & Economic Statistics, 40(3), 1094–1106.
Meinshausen, N. (2006). “Quantile regression forests.” Journal of Machine Learning Research, 7, 983–999.
Goulet Coulombe, P. (2024). “The macroeconomy as a random forest.” Journal of Applied Econometrics, 39(3), 401–421.
Goulet Coulombe, P. (2025). “A neural Phillips curve and a deep output gap.” Journal of Business & Economic Statistics, 43(3), 669–683.

Why should you attend BSE Executive Education courses?

All BSE Executive Education courses are taught to the same high standard as our Master’s programs.

1

Network with like-minded peers from around the world

2

Short courses allow you to learn without a big time commitment

3

Try something new and expand your knowledge and career prospects, or advance your thesis

Testimonials

IK

Ioannis Krompas

The course provided valuable insights into leveraging high-frequency data for real-time economic forecasting. I gained a deeper understanding of nowcasting techniques and their practical applications, which I look forward to applying.

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PR

Pablo Ruiz

This course helped me stay up to date with the state of the art in forecasting and refresh my knowledge, which I hadn’t practiced in a while. The course has great teachers, a rigorous academic curriculum, and excellent student support throughout. I highly recommend it.

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Admissions

If you want to apply for this Nowcasting and Forecasting course, ensure you meet the criteria below.

Next edition: March 8-19, 2027

Early bird deadline: January 20, 2027

Requirements

Candidates are assessed on an individual basis according to their professional or academic background
Students must have their own laptop or desktop computer and a good Internet connection to be able to follow and fully benefit from the course

Nowcasting and Forecasting with High-Frequency Real-Time Data

Knowledge of mathematics and/or statistics at a graduate level could help to take further advantage of the course, although it is not mandatory
Code is provided and worked through in class

Course Schedule

The times listed are Central European Time (CET). Compare with your time zone on time.is

Instructors, topics, and schedules are subject to change.

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Week 2

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Certificate and Fees

Certificate

Participants who attend at least 80% of the course will receive a Certificate of Attendance free of charge. Participants will not be graded or assessed during the course.

Fees

A 10% discount applies when the confirmation payment is completed on or before the announced Early Bird deadline.

Multiple course discounts are available. Find out more information in our Fees and Discounts pdf.

Fees for courses in other Executive Education programs may vary.

Course

Nowcasting and Forecasting with High-Frequency Real-Time Data

Modality

Online

Total Hours

20

ECTS

0

Regular Fee

Reduced Fee*

*Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

FAQ

Need more information? Check out our most frequently asked questions.

See the full Executive Education calendar

The full calendar is available to view here.

Are the sessions recorded?

Sessions will be recorded and videos will be available for a month once the course has finished.

How much does each Executive Education course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Executive Education courses. See more information about available discounts or request a personalized discount quote by email.